Learning lemma support graphs in Quip and IC3
Bibliographic record
Abstract
Formal verification is one of the fastest growing fields in verification. The Boolean satisfiability-based unbounded model checking algorithm of IC3 has become widely applied in industry and is frequently used as a subroutine in other formal verification algorithms, such as FAIR and IICTL. Any improvement to IC3 can therefore yield substantial benefits in many areas of formal verification. Towards that end, this paper introduces the notion of a support graph, which is applied in IC3. Techniques are presented to compute the support graph by modifying the satisfiability queries used in IC3 at the cost of a modest increase in runtime. It is used to increase the re-use of information across runs of the model checker, thereby improving runtime performance in incremental model checking. It can also be applied within a single run of the model checker to avoid unnecessary queries to the satisfiability solver and accelerate the discovery of a proof. Experiments are presented on HWMCC'15 circuits demonstrating the benefits of the presented approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".